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Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)

Author

Listed:
  • Yili Chen

    (School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao 999078, China)

  • Congdong Li

    (School of Business, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao 999078, China
    The School of Management, Jinan University, Zhuhai 519000, China)

  • Han Wang

    (The Faculty of Data Science, City University of Macau, Macao 999078, China
    The Department of Artificial Intelligence and Big Data Applications, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai 519000, China
    The School of Computer, College of Beijing University of Technology Zhuhai, Zhuhai 519000, China)

Abstract

Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.

Suggested Citation

  • Yili Chen & Congdong Li & Han Wang, 2022. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)," Forecasting, MDPI, vol. 4(4), pages 1-20, September.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:42-786:d:923606
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
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